cd ../..

/Users/shanekercheval/repos/data-science-template

%run "source/config/notebook_settings.py"
import os
import mlflow
from mlflow.tracking import MlflowClient
from helpsk.utility import read_pickle
import helpsk as hlp

from source.library.utilities import Timer, log_info, get_config

config = get_config()
mlflow_uri = config['MLFLOW']['URI']
log_info(f"MLFlow URI: {mlflow_uri}")

client = MlflowClient(tracking_uri='http://127.0.0.1:1234')

2022-06-10 14:46:55 - INFO     | MLFlow URI: http://127.0.0.1:1234

Get Latest Experiment Run from MLFlow

credit_experiment = client.get_experiment_by_name(name=config['MLFLOW']['EXPERIMENT_NAME'])
runs = client.list_run_infos(experiment_id=credit_experiment.experiment_id)
latest_run = runs[np.argmax([x.start_time for x in runs])]

yaml_path = client.download_artifacts(run_id=latest_run.run_id, path='experiment.yaml')
results = hlp.sklearn_eval.MLExperimentResults.from_yaml_file(yaml_file_name = yaml_path)

best_estimator = read_pickle(client.download_artifacts(
    run_id=latest_run.run_id,
    path='experiment_best_estimator.pkl'
))

best_estimator

Pipeline(steps=[('prep',
                 ColumnTransformer(transformers=[('numeric',
                                                  Pipeline(steps=[('imputer',
                                                                   TransformerChooser(transformer=SimpleImputer(strategy='median'))),
                                                                  ('scaler',
                                                                   TransformerChooser()),
                                                                  ('pca',
                                                                   TransformerChooser(transformer=PCA(n_components='mle')))]),
                                                  ['duration', 'credit_amount',
                                                   'installment_commitment',
                                                   'residence_since', 'age',
                                                   'existing_credi...
                                                   'savings_status',
                                                   'employment',
                                                   'personal_status',
                                                   'other_parties',
                                                   'property_magnitude',
                                                   'other_payment_plans',
                                                   'housing', 'job',
                                                   'own_telephone',
                                                   'foreign_worker'])])),
                ('model',
                 RandomForestClassifier(criterion='entropy', max_depth=70,
                                        max_features=0.1142268477118407,
                                        max_samples=0.5483119512487002,
                                        min_samples_leaf=8,
                                        min_samples_split=12, n_estimators=553,
                                        random_state=42))])

In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook.
On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.

Training & Test Data Info

client.download_artifacts(run_id=latest_run.run_id, path='x_train.pkl')

'/Users/shanekercheval/repos/data-science-template/mlflow-artifact-root/1/2702b1f57ead444a81097fc2673ef18d/artifacts/x_train.pkl'

with Timer("Loading training/test datasets"):
    X_train = pd.pandas.read_pickle(client.download_artifacts(run_id=latest_run.run_id, path='x_train.pkl'))
    X_test = pd.pandas.read_pickle(client.download_artifacts(run_id=latest_run.run_id, path='x_test.pkl'))
    y_train = pd.pandas.read_pickle(client.download_artifacts(run_id=latest_run.run_id, path='y_train.pkl'))
    y_test = pd.pandas.read_pickle(client.download_artifacts(run_id=latest_run.run_id, path='y_test.pkl'))

2022-06-10 14:46:55 - INFO     | *****Timer Started: Loading training/test datasets
2022-06-10 14:46:55 - INFO     | *****Timer Finished (0.00 seconds)

log_info(X_train.shape)
log_info(len(y_train))

log_info(X_test.shape)
log_info(len(y_test))

2022-06-10 14:46:55 - INFO     | (800, 20)
2022-06-10 14:46:55 - INFO     | 800
2022-06-10 14:46:55 - INFO     | (200, 20)
2022-06-10 14:46:55 - INFO     | 200

np.unique(y_train, return_counts=True)

(array([0, 1]), array([559, 241]))

np.unique(y_train, return_counts=True)[1] / np.sum(np.unique(y_train, return_counts=True)[1])

array([0.69875, 0.30125])

np.unique(y_test, return_counts=True)[1] / np.sum(np.unique(y_test, return_counts=True)[1])

array([0.705, 0.295])

Cross Validation Results

Best Scores/Params

log_info(f"Best Score: {results.best_score}")

2022-06-10 14:46:55 - INFO     | Best Score: 0.7626843675980791

log_info(f"Best Params: {results.best_params}")

2022-06-10 14:46:55 - INFO     | Best Params: {'model': 'RandomForestClassifier()', 'max_features': 0.1142268477118407, 'max_depth': 70, 'n_estimators': 553, 'min_samples_split': 12, 'min_samples_leaf': 8, 'max_samples': 0.5483119512487002, 'criterion': 'entropy', 'imputer': "SimpleImputer(strategy='median')", 'scaler': 'None', 'pca': "PCA('mle')", 'encoder': 'OneHotEncoder()'}

# Best model from each model-type.
df = results.to_formatted_dataframe(return_style=False, include_rank=True)
df["model_rank"] = df.groupby("model")["roc_auc Mean"].rank(method="first", ascending=False)
df.query('model_rank == 1')

	rank	roc_auc Mean	roc_auc 95CI.LO	roc_auc 95CI.HI	model	C	max_features	max_depth	n_estimators	min_samples_split	min_samples_leaf	max_samples	criterion	learning_rate	min_child_weight	subsample	colsample_bytree	colsample_bylevel	reg_alpha	reg_lambda	imputer	scaler	pca	encoder	model_rank
13	1	0.76	0.71	0.81	RandomForestClassifier()	NaN	0.11	70.00	553.00	12.00	8.00	0.55	entropy	NaN	NaN	NaN	NaN	NaN	NaN	NaN	SimpleImputer(strategy='median')	None	PCA('mle')	OneHotEncoder()	1.00
0	3	0.76	0.71	0.81	LogisticRegression()	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	SimpleImputer()	StandardScaler()	None	OneHotEncoder()	1.00
7	4	0.76	0.71	0.80	ExtraTreesClassifier()	NaN	0.70	38.00	558.00	18.00	1.00	0.51	gini	NaN	NaN	NaN	NaN	NaN	NaN	NaN	SimpleImputer()	None	PCA('mle')	OneHotEncoder()	1.00
19	11	0.73	0.70	0.76	XGBClassifier()	NaN	NaN	15.00	1159.00	NaN	NaN	NaN	NaN	0.03	29.00	0.83	0.52	0.50	0.00	1.84	SimpleImputer(strategy='median')	None	PCA('mle')	CustomOrdinalEncoder()	1.00

results.to_formatted_dataframe(return_style=True,
                               include_rank=True,
                               num_rows=500)

rank	roc_auc Mean	roc_auc 95CI.LO	roc_auc 95CI.HI	model	C	max_features	max_depth	n_estimators	min_samples_split	min_samples_leaf	max_samples	criterion	learning_rate	min_child_weight	subsample	colsample_bytree	colsample_bylevel	reg_alpha	reg_lambda	imputer	scaler	pca	encoder
1	0.763	0.712	0.814	RandomForestClassifier()	<NA>	0.114	70.000	553.000	12.000	8.000	0.548	entropy	<NA>	<NA>	<NA>	<NA>	<NA>	<NA>	<NA>	SimpleImputer(strategy='median')	None	PCA('mle')	OneHotEncoder()
2	0.759	0.699	0.819	RandomForestClassifier()	<NA>	0.030	84.000	1,088.000	24.000	36.000	0.981	gini	<NA>	<NA>	<NA>	<NA>	<NA>	<NA>	<NA>	SimpleImputer()	None	None	OneHotEncoder()
3	0.759	0.713	0.805	LogisticRegression()	<NA>	<NA>	<NA>	<NA>	<NA>	<NA>	<NA>	<NA>	<NA>	<NA>	<NA>	<NA>	<NA>	<NA>	<NA>	SimpleImputer()	StandardScaler()	None	OneHotEncoder()
4	0.759	0.714	0.804	ExtraTreesClassifier()	<NA>	0.700	38.000	558.000	18.000	1.000	0.514	gini	<NA>	<NA>	<NA>	<NA>	<NA>	<NA>	<NA>	SimpleImputer()	None	PCA('mle')	OneHotEncoder()
5	0.756	0.726	0.786	RandomForestClassifier()	<NA>	<NA>	<NA>	<NA>	<NA>	<NA>	<NA>	<NA>	<NA>	<NA>	<NA>	<NA>	<NA>	<NA>	<NA>	SimpleImputer()	None	None	OneHotEncoder()
6	0.752	0.684	0.819	LogisticRegression()	0.001	<NA>	<NA>	<NA>	<NA>	<NA>	<NA>	<NA>	<NA>	<NA>	<NA>	<NA>	<NA>	<NA>	<NA>	SimpleImputer(strategy='median')	MinMaxScaler()	None	OneHotEncoder()
7	0.744	0.709	0.780	RandomForestClassifier()	<NA>	0.681	38.000	1,461.000	23.000	10.000	0.553	gini	<NA>	<NA>	<NA>	<NA>	<NA>	<NA>	<NA>	SimpleImputer(strategy='median')	None	None	CustomOrdinalEncoder()
8	0.742	0.689	0.796	ExtraTreesClassifier()	<NA>	<NA>	<NA>	<NA>	<NA>	<NA>	<NA>	<NA>	<NA>	<NA>	<NA>	<NA>	<NA>	<NA>	<NA>	SimpleImputer()	None	None	OneHotEncoder()
9	0.742	0.703	0.781	ExtraTreesClassifier()	<NA>	0.473	25.000	834.000	48.000	6.000	0.631	gini	<NA>	<NA>	<NA>	<NA>	<NA>	<NA>	<NA>	SimpleImputer()	None	PCA('mle')	CustomOrdinalEncoder()
10	0.741	0.689	0.793	RandomForestClassifier()	<NA>	0.559	72.000	1,330.000	35.000	32.000	0.630	gini	<NA>	<NA>	<NA>	<NA>	<NA>	<NA>	<NA>	SimpleImputer(strategy='most_frequent')	None	PCA('mle')	OneHotEncoder()
11	0.726	0.698	0.755	XGBClassifier()	<NA>	<NA>	15.000	1,159.000	<NA>	<NA>	<NA>	<NA>	0.032	29.000	0.834	0.520	0.503	0.003	1.839	SimpleImputer(strategy='median')	None	PCA('mle')	CustomOrdinalEncoder()
12	0.726	0.689	0.762	LogisticRegression()	0.000	<NA>	<NA>	<NA>	<NA>	<NA>	<NA>	<NA>	<NA>	<NA>	<NA>	<NA>	<NA>	<NA>	<NA>	SimpleImputer(strategy='median')	StandardScaler()	None	CustomOrdinalEncoder()
13	0.723	0.637	0.809	LogisticRegression()	0.403	<NA>	<NA>	<NA>	<NA>	<NA>	<NA>	<NA>	<NA>	<NA>	<NA>	<NA>	<NA>	<NA>	<NA>	SimpleImputer(strategy='median')	StandardScaler()	None	CustomOrdinalEncoder()
14	0.723	0.666	0.779	ExtraTreesClassifier()	<NA>	0.857	30.000	879.000	17.000	28.000	0.563	entropy	<NA>	<NA>	<NA>	<NA>	<NA>	<NA>	<NA>	SimpleImputer(strategy='median')	None	None	CustomOrdinalEncoder()
15	0.722	0.658	0.786	ExtraTreesClassifier()	<NA>	0.781	50.000	590.000	35.000	47.000	0.846	gini	<NA>	<NA>	<NA>	<NA>	<NA>	<NA>	<NA>	SimpleImputer(strategy='median')	None	PCA('mle')	OneHotEncoder()
16	0.720	0.699	0.741	XGBClassifier()	<NA>	<NA>	19.000	1,217.000	<NA>	<NA>	<NA>	<NA>	0.042	4.000	0.893	0.936	0.652	0.029	3.708	SimpleImputer(strategy='most_frequent')	None	None	CustomOrdinalEncoder()
17	0.720	0.638	0.801	LogisticRegression()	3.489	<NA>	<NA>	<NA>	<NA>	<NA>	<NA>	<NA>	<NA>	<NA>	<NA>	<NA>	<NA>	<NA>	<NA>	SimpleImputer(strategy='median')	StandardScaler()	PCA('mle')	CustomOrdinalEncoder()
18	0.718	0.695	0.740	XGBClassifier()	<NA>	<NA>	<NA>	<NA>	<NA>	<NA>	<NA>	<NA>	<NA>	<NA>	<NA>	<NA>	<NA>	<NA>	<NA>	SimpleImputer()	None	None	OneHotEncoder()
19	0.716	0.666	0.766	XGBClassifier()	<NA>	<NA>	12.000	1,471.000	<NA>	<NA>	<NA>	<NA>	0.126	8.000	0.912	0.569	0.821	0.045	1.029	SimpleImputer()	None	PCA('mle')	CustomOrdinalEncoder()
20	0.711	0.659	0.764	XGBClassifier()	<NA>	<NA>	5.000	1,218.000	<NA>	<NA>	<NA>	<NA>	0.115	2.000	0.545	0.648	0.852	0.123	1.165	SimpleImputer(strategy='median')	None	PCA('mle')	CustomOrdinalEncoder()

results.to_formatted_dataframe(query='model == "RandomForestClassifier()"', include_rank=True)

rank	roc_auc Mean	roc_auc 95CI.LO	roc_auc 95CI.HI	max_features	max_depth	n_estimators	min_samples_split	min_samples_leaf	max_samples	criterion	imputer	pca	encoder
1	0.763	0.712	0.814	0.114	70.000	553.000	12.000	8.000	0.548	entropy	SimpleImputer(strategy='median')	PCA('mle')	OneHotEncoder()
2	0.759	0.699	0.819	0.030	84.000	1,088.000	24.000	36.000	0.981	gini	SimpleImputer()	None	OneHotEncoder()
3	0.756	0.726	0.786	<NA>	<NA>	<NA>	<NA>	<NA>	<NA>	<NA>	SimpleImputer()	None	OneHotEncoder()
4	0.744	0.709	0.780	0.681	38.000	1,461.000	23.000	10.000	0.553	gini	SimpleImputer(strategy='median')	None	CustomOrdinalEncoder()
5	0.741	0.689	0.793	0.559	72.000	1,330.000	35.000	32.000	0.630	gini	SimpleImputer(strategy='most_frequent')	PCA('mle')	OneHotEncoder()

results.to_formatted_dataframe(query='model == "LogisticRegression()"', include_rank=True)

rank	roc_auc Mean	roc_auc 95CI.LO	roc_auc 95CI.HI	C	imputer	scaler	pca	encoder
1	0.759	0.713	0.805	<NA>	SimpleImputer()	StandardScaler()	None	OneHotEncoder()
2	0.752	0.684	0.819	0.001	SimpleImputer(strategy='median')	MinMaxScaler()	None	OneHotEncoder()
3	0.726	0.689	0.762	0.000	SimpleImputer(strategy='median')	StandardScaler()	None	CustomOrdinalEncoder()
4	0.723	0.637	0.809	0.403	SimpleImputer(strategy='median')	StandardScaler()	None	CustomOrdinalEncoder()
5	0.720	0.638	0.801	3.489	SimpleImputer(strategy='median')	StandardScaler()	PCA('mle')	CustomOrdinalEncoder()

BayesSearchCV Performance Over Time

results.plot_performance_across_trials(facet_by='model').show()

results.plot_performance_across_trials(query='model == "RandomForestClassifier()"').show()

---

Variable Performance Over Time

results.plot_parameter_values_across_trials(query='model == "RandomForestClassifier()"').show()

---

Scatter Matrix

# results.plot_scatter_matrix(query='model == "RandomForestClassifier()"',
#                             height=1000, width=1000).show()

---

Variable Performance - Numeric

results.plot_performance_numeric_params(query='model == "RandomForestClassifier()"',
                                        height=800)

results.plot_parallel_coordinates(query='model == "RandomForestClassifier()"').show()

---

Variable Performance - Non-Numeric

results.plot_performance_non_numeric_params(query='model == "RandomForestClassifier()"').show()

---

results.plot_score_vs_parameter(
    query='model == "RandomForestClassifier()"',
    parameter='max_features',
    size='max_depth',
    color='encoder',
)

---

# results.plot_parameter_vs_parameter(
#     query='model == "XGBClassifier()"',
#     parameter_x='colsample_bytree',
#     parameter_y='learning_rate',
#     size='max_depth'
# )

# results.plot_parameter_vs_parameter(
#     query='model == "XGBClassifier()"',
#     parameter_x='colsample_bytree',
#     parameter_y='learning_rate',
#     size='imputer'
# )

---

Best Model - Test Set Performance

test_predictions = best_estimator.predict_proba(X_test)[:, 1]
test_predictions[0:10]

array([0.37346429, 0.4104999 , 0.48390757, 0.35895319, 0.17376038,
       0.32902654, 0.17785134, 0.40538264, 0.21201897, 0.24403248])

evaluator = hlp.sklearn_eval.TwoClassEvaluator(
    actual_values=y_test,
    predicted_scores=test_predictions,
    score_threshold=0.37
)

evaluator.plot_actual_vs_predict_histogram()

evaluator.plot_confusion_matrix()

evaluator.all_metrics_df(return_style=True,
                         dummy_classifier_strategy=['prior', 'constant'],
                         round_by=3)

	Score	Dummy (prior)	Dummy (constant)	Explanation
AUC	0.794	0.500	0.500	Area under the ROC curve (true pos. rate vs false pos. rate); ranges from 0.5 (purely random classifier) to 1.0 (perfect classifier)
True Positive Rate	0.593	0.000	1.000	59.3% of positive instances were correctly identified.; i.e. 35 "Positive Class" labels were correctly identified out of 59 instances; a.k.a Sensitivity/Recall
True Negative Rate	0.837	1.000	0.000	83.7% of negative instances were correctly identified.; i.e. 118 "Negative Class" labels were correctly identified out of 141 instances
False Positive Rate	0.163	0.000	1.000	16.3% of negative instances were incorrectly identified as positive; i.e. 23 "Negative Class" labels were incorrectly identified as "Positive Class", out of 141 instances
False Negative Rate	0.407	1.000	0.000	40.7% of positive instances were incorrectly identified as negative; i.e. 24 "Positive Class" labels were incorrectly identified as "Negative Class", out of 59 instances
Positive Predictive Value	0.603	0.000	0.295	When the model claims an instance is positive, it is correct 60.3% of the time; i.e. out of the 58 times the model predicted "Positive Class", it was correct 35 times; a.k.a precision
Negative Predictive Value	0.831	0.705	0.000	When the model claims an instance is negative, it is correct 83.1% of the time; i.e. out of the 142 times the model predicted "Negative Class", it was correct 118 times
F1 Score	0.598	0.000	0.456	The F1 score can be interpreted as a weighted average of the precision and recall, where an F1 score reaches its best value at 1 and worst score at 0.
Precision/Recall AUC	0.642	0.295	0.295	Precision/Recall AUC is calculated with `average_precision` which summarizes a precision-recall curve as the weighted mean of precisions achieved at each threshold. See sci-kit learn documentation for caveats.
Accuracy	0.765	0.705	0.295	76.5% of instances were correctly identified
Error Rate	0.235	0.295	0.705	23.5% of instances were incorrectly identified
% Positive	0.295	0.295	0.295	29.5% of the data are positive; i.e. out of 200 total observations; 59 are labeled as "Positive Class"
Total Observations	200	200	200	There are 200 total observations; i.e. sample size

evaluator.plot_roc_auc_curve().show()

<Figure size 720x444.984 with 0 Axes>

evaluator.plot_precision_recall_auc_curve().show()

evaluator.plot_threshold_curves(score_threshold_range=(0.1, 0.7)).show()

evaluator.plot_precision_recall_tradeoff(score_threshold_range=(0.1, 0.6)).show()

evaluator.calculate_lift_gain(return_style=True)

	Gain	Lift
Percentile
5	0.17	3.39
10	0.25	2.54
15	0.32	2.15
20	0.42	2.12
25	0.53	2.10
30	0.61	2.03
35	0.68	1.94
40	0.75	1.86
45	0.76	1.69
50	0.80	1.59
55	0.80	1.45
60	0.88	1.47
65	0.88	1.36
70	0.92	1.31
75	0.93	1.24
80	0.97	1.21
85	0.98	1.16
90	1.00	1.11
95	1.00	1.05
100	1.00	1.00

---